Automatic measurement of an evoked neural response concurrent with an indication of a psychophysics reaction

ABSTRACT

A method of fitting to a recipient a cochlear implant including a plurality of stimulation channels. The method comprises applying to at least one stimulation channel of the cochlear implant a first stimulus signal comprising a neural response component and a psychophysics signal component, automatically measuring a strength of an evoked compound action potential (ECAP) of the recipient evoked in response to the application of the neural response component of the first stimulus signal, and simultaneously providing to a user an indication of the automatically measured strength of the ECAP and a value of the applied psychophysics signal component.

CROSS-REFERENCE TO RELATED APPLICATIONS

This application is a continuation-in-part of U.S. Utility patent application Ser. No. 12/886,378, filed Sep. 20, 2010, which is a continuation of U.S. Utility patent application Ser. No. 11/555,219, filed Oct. 31, 2006, now U.S. Pat. No. 7,801,617, which claims the priority of U.S. Provisional Patent Application 60/731,253, filed on Oct. 31, 2005. This application is also a continuation-in-part of U.S. Utility patent application Ser. No. 10/569,054, filed Feb. 22, 2006, which is a national stage application under 35 USC §371(c) of International Patent Application PCT/US2005/021207, filed Jun. 15, 2005, which claims the priority of Australian Provisional Application 2004903254, filed Jun. 15, 2004. The content of these applications is hereby incorporated by reference herein.

BACKGROUND

1. Field of the Invention

The present invention relates generally to the measurement of a neural response evoked by electrical stimulation and, more particularly, to the automatic measurement of an evoked neural response concurrent with an indication of a psychophysics reaction.

2. Related Art

Hearing loss, which may be due to many different causes, is generally of two types, conductive and sensorineural. In some cases, a person may have hearing loss of both types. Conductive hearing loss occurs when the normal mechanical pathways for sound to reach the hair cells in the cochlea are impeded, for example, by damage to the ossicles. Conductive hearing loss is often addressed with conventional auditory prostheses commonly referred to as hearing aids, which amplify sound so that acoustic information can reach the cochlea.

In many people who are profoundly deaf, however, the reason for their deafness is sensorineural hearing loss. This type of hearing loss is due to the absence or destruction of the hair cells in the cochlea which transduce acoustic signals into nerve impulses. Those suffering from sensorineural hearing loss are thus unable to derive suitable benefit from conventional hearing aids due to the damage to or absence of the mechanism for naturally generating nerve impulses from sound.

It is for this purpose that another type of auditory prosthesis, a Cochlear™ implant (also commonly referred to as Cochlear™ prostheses, Cochlear™ devices, Cochlear™ implant devices, and the like; generally and collectively referred to as “cochlear implants” herein) has been developed. These types of auditory prostheses bypass the hair cells in the cochlea, directly delivering electrical stimulation to the auditory nerve fibers via an implanted electrode assembly. This enables the brain to perceive a hearing sensation resembling the natural hearing sensation normally delivered to the auditory nerve.

Cochlear implants have traditionally comprised an external speech processor unit worn on the body of the recipient and a receiver/stimulator unit implanted in the mastoid bone of the recipient. The external speech processor detects external sound and converts the detected sound into a coded signal through an appropriate speech processing strategy. The coded signal is sent to the implanted receiver/stimulator unit via a transcutaneous link. The receiver/stimulator unit processes the coded signal to generate a series of stimulation sequences which are then applied directly to the auditory nerve via a series-arrangement or an array of electrodes positioned within the cochlea.

More recently, the external speech processor and implanted stimulator unit may be combined to produce a totally implantable cochlear implant capable of operating, at least for a period of time, without the need for an external device. In such an implant, a microphone would be implanted within the body of the recipient, for example in the ear canal or within the stimulator unit. Detected sound is directly processed by a speech processor within the stimulator unit, with the subsequent stimulation signals delivered without the need for any transcutaneous transmission of signals.

Generally, there is a need to obtain data from the implanted components of a cochlear implant. Such data collection enables detection and confirmation of the normal operation of the device, and allows stimulation parameters to be optimized to suit the needs of individual recipients. This includes data relating to the response of the auditory nerve to stimulation, which is of particular relevance to the present invention. Thus, regardless of the particular configuration, cochlear implants generally have the capability to communicate with an external device such as for program upgrades and/or implant interrogation, and to read and/or alter the operating parameters of the device.

Determining the response of an auditory nerve to stimulation has been addressed with limited success in conventional systems. Typically, following the surgical implantation of a cochlear implant, the implant is fitted or customized to conform to the specific recipient demands. This process of configuring an implanted hearing prosthesis for a particular recipient (also referred to as patient) is commonly referred to as “fitting” the implant to the recipient. This involves the collection and determination of recipient-specific parameters such as threshold levels (T levels) and maximum comfort levels (C levels) for each stimulation channel. This collection of recipient-specific parameters, including the T and C levels for a plurality of stimulation channels, is commonly referred to as a MAP.

Essentially, the procedure is performed manually by a clinician by taking psychophysics measurements by applying stimulation pulses for each channel and receiving an indication from the implant recipient as to the level and comfort of the resulting sound. This process typically involves subjectively interpreting a behavioural indication from the implant recipient as to the threshold and comfort levels of the perceived sound. For implants with a large number of channels for stimulation, this process is quite time consuming and rather subjective as it relies heavily on the recipient's subjective impression of the stimulation rather than any objective measurement.

This psychophysics approach is further limited in the case of children and prelingually or congenitally deaf recipients who are unable to supply an accurate impression of the resultant hearing sensation, and hence fitting of the implant may be sub-optimal. In such cases an incorrectly-fitted cochlear implant may result in the recipient not receiving optimum benefit from the implant, and in the cases of children, may directly hamper the speech and hearing development of the child. Therefore, there is a need to obtain objective measurements of recipient-specific data, especially in cases where an accurate subjective measurement is not possible.

One proposed method of interrogating the performance of an implanted cochlear implant and making objective measurements of recipient-specific data such as T and C levels is to directly measure the response of the auditory nerve to an electrical stimulus. The direct measurement of neural responses, commonly referred to as Electrically-evoked Compound Action Potentials (ECAPs) in the context of cochlear implants, provides an objective measurement of the response of the nerve to electrical stimulus. Following electrical stimulation, the neural response is caused by the superposition of single cell responses at the outside of neuron membranes. The measured neural response is transmitted to an externally-located system, typically via a telemetry system. Such Neural Response Telemetry (NRT™) provides measurements of the ECAPs from within the cochlea in response to various stimulations. The measurements taken to determine whether a neural response or ECAPs has occurred are referred to herein by the common vernacular NRT measurements. Generally, the neural response resulting from a stimulus presented at one electrode is measured at a neighbouring electrode, although this need not be the case.

A sequence 100 of NRT measurements 102 is shown in FIG. 1A. Sequence 100 contains seven NRT measurements 102A-102G which display a good neural response. Each NRT measurement waveform 102A-102G comprises a clear negative peak (N1) 104A and positive peak (P1) 106A. Only one positive and negative peak is shown in FIG. 1A for clarity. As used herein, a “good” neural response is one which approximates a true neural response to an applied stimulus current.

An NRT measurement waveform may have a partial N1 peak, no P1 peak or a double positive peak P1 and P2 and still represent a good neural response. The measurement waveforms 102 toward the top of the graph depicted in FIG. 1A (measurement waveforms 102A, 102B, for example) indicates a stronger neural response to a relatively large neural stimulus, while the measurement waveforms toward the bottom of the graph (measurement waveforms 102F and 102G, for example) indicate a weaker neural response with reduced neural stimuli strengths.

Two sequences 120A and 120B of seven (7) NRT measurements 122A-122G that display the absence of a neural response are shown in FIG. 1B. In the left-hand sequence 120A, stimulus artefact and/or noise are observed. The stimulus artefact may give the impression of artificial peaks which may be interpreted as a neural response to a previously applied stimulus signal. In right-hand sequence 120B, noise is observed.

Distinguishing between measurements that display a neural response such as those of FIG. 1A, and measurements which do not display a neural response such as those of FIG. 1B, is an important aspect of performing NRT measurements. This task can be extremely difficult, for instance when the combination of stimulus artefact and noise gives the appearance of a weak neural response.

In particular, the minimum stimulus current level required to evoke a neural response at a given electrode is referred to herein as the threshold NRT level, or T-NRT. In general, T-NRT profiles are correlated with MAP T and C profiles, and thus T-NRT levels can be used as a guide for MAP fitting. Accordingly, accurate determination of T-NRT values for each electrode and for each recipient is highly desirable.

One conventional approach to determine T-NRT values is the Amplitude Growth Function (AGF) method. The AGF method is based on the premise that the peak-to-peak amplitude of a neural response increases linearly with stimulus current level. It should be appreciated, however, that the relationship is more accurately defined by a sigmoidal function. By obtaining the value at different stimulus current levels, a regression line may be drawn through these measurement points and extrapolated to the point at which the peak-to-peak amplitude becomes zero, thus indicating the threshold stimulus level.

For example, FIG. 2 illustrates a typical, non-linear, measurement set of peak-to-peak amplitude (in microvolts) vs. current level (in digitized current level units). As is well known in the art, there is a one-to-one exponential relationship between the unit of current level and the conventional unit of current (the ampere). In one embodiment, the current level scale is from 0 to 255 with each unit representing an increasingly lager quantity of amperes. This single set of measurements 200 (only one of which is referenced in FIG. 2 for ease of illustration) can be fitted with a number of regression lines 202A, 202B, and 202C, yielding possible T-NRT values of 125, 135 and 148 current level units, a variation of over 18%. This is because AGF is observer-dependent when selecting the measurement points to include in regression.

In addition, the AGF approach requires a significant number of NRT measurements above the threshold to enable a regression line to be determined. Such measurements may be beyond the recipient's loudest acceptable or comfort level, and thus the ability to post-operatively obtain such measurements is limited. Additionally, such measurements do not yield a simple linear relationship, and typically various regression lines can be determined resulting in significantly different T-NRT levels from a given measurement set.

Visual detection of T-NRT levels is a more fundamental conventional approach. NRT measurements of increasing stimulus level are performed until the stimulus level at which a neural response is detected, at which point the T-NRT level is defined as the stimulus level. Visual detection depends critically on the acuity of the observer to distinguish between neural responses and artefact or noise. Visual detection of threshold is also observer-dependent.

The presence of stimulus artefacts and noise in measurements of an evoked neural response can lead to an incorrect determination of whether a neural response, or ECAP, has occurred in the above conventional systems. Accordingly, there is a need to objectively and accurately detect T-NRT thresholds to facilitate neural response determinations.

Indeed, there is a need to accurately measure the response of nerves to electrical stimulation in stimulating medical devices that deliver electrical stimulation to other neural regions of a recipient such as the central nervous system (including the brain and spinal cord), as well as the peripheral nervous system (including the autonomic and sensory-somatic nervous systems). Thus, the accurate measurement of a neural response may provide a useful objective measurement of the effectiveness of the stimulation in many applications.

SUMMARY

In one aspect of the invention, there is a method of fitting to a recipient a cochlear implant including a plurality of stimulation channels. The method comprises applying to at least one stimulation channel of the cochlear implant a first stimulus signal comprising a neural response component and a psychophysics signal component, automatically measuring an evoked compound action potential (ECAP) of the recipient evoked in response to the application of the neural response component of the first stimulus signal, and simultaneously providing to a user an indication of the automatically measured ECAP and a value of the applied psychophysics signal component.

According to another aspect of the invention, there is a method of fitting to a recipient a cochlear implant including a plurality of stimulation channels. The method comprises applying a stimulus signal to a target neural region of the recipient of the cochlear implant via a stimulus signal, the stimulus signal comprising a neural response component and a psychophysics signal component. In the method, applying the stimulus signal includes applying the neural response component in a burst, applying the stimulus signal includes applying the psychophysics signal component in a burst, and the bursts are applied temporally adjacent to each other.

According to another aspect of the invention, there is an apparatus for fitting to a recipient a cochlear implant including a plurality of stimulation channels. The apparatus is configured to apply to at least one stimulation channel of the cochlear implant a first stimulus signal comprising a neural response component and a psychophysics signal component, automatically measure a strength of an evoked compound action potential (ECAP) of the recipient evoked in response to the application of the neural response component of the first stimulus, and simultaneously automatically provide to a user of the apparatus an indication of the automatically measured strength of the ECAP and a value of the applied psychophysics signal component.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1A is a graph illustrating exemplary measurements obtained of neural responses evoked by varying stimulus levels;

FIG. 1B is a graph illustrating exemplary measurements of neural response showing stimulus artefact and noise;

FIG. 2 is a graph of peak-to-peak evoked neural response amplitude vs. stimulus current level, showing possible regression lines;

FIG. 3A is a perspective view of a cochlear implant system including a cochlear implant coupled to an expert system in which embodiments of the present invention are implemented;

FIG. 3B is a schematic diagram illustrating one fitting arrangement configured to fit a cochlear implant to a recipient;

FIG. 4A is a high-level flow chart in accordance with one embodiment of the present invention;

FIG. 4B is a high-level flow chart in accordance with an alternative embodiment of the present invention;

FIG. 5A is a flowchart illustrating an algorithm by which a minimum stimulus threshold may be determined in accordance with an embodiment of the invention;

FIG. 5B is a flowchart illustrating an algorithm by which a minimum stimulus threshold may be determined in accordance with an embodiment of the invention;

FIG. 5C is a flowchart showing an algorithm by which the stimulus current level is incremented in accordance with an embodiment of the invention;

FIG. 6A illustrates one embodiment of a decision tree used in the embodiment of FIG. 5A to determine whether a neural response has been evoked;

FIG. 6B illustrates one embodiment of a decision tree used in the embodiment of FIG. 5B to determine whether a neural response has been evoked;

FIG. 7 is a flowchart illustrating an automated algorithm for optimization of a third phase compensatory stimulus in accordance with one embodiment of the present invention;

FIG. 8 is a flowchart illustrating the derivation of the minimum stimulus threshold T-NRT from MaxT-NRT and MinT-NRT values, in accordance with one embodiment of the present invention;

FIG. 9 is a graph illustrating a predefined expected or ‘good’ neural response used for comparison by a portion of the decision tree of FIG. 6;

FIG. 10 is a graph illustrating a predefined expected or ‘good’ neural response plus stimulus artefact used for comparison by a portion of the decision tree of FIG. 6;

FIG. 11 illustrates a predefined expected or ‘good’ stimulus artefact;

FIG. 12 is a stimulus graph of a two-component stimulus signal used in accordance with an embodiment of the present invention;

FIG. 13A is a display providing an indication of a concurrent measurement of evoked compound action potential (ECAP) and psychophysics response in accordance with one embodiment of the present invention;

FIG. 13B is a display providing an indication of a concurrent measurement of evoked compound action potential (ECAP) and psychophysics response in accordance with one embodiment of the present invention; and

FIG. 13C is a display providing an indication of a concurrent measurement of evoked compound action potential (ECAP) and psychophysics response in accordance with one embodiment of the present invention.

DETAILED DESCRIPTION

An embodiment of the present invention is directed to a method of fitting to a recipient a cochlear implant including a plurality of stimulation channels. An embodiment of the present invention also includes an apparatus for executing the method. The method includes applying to at least one stimulation channel of the cochlear implant a first stimulus signal comprising a neural response component and a psychophysics signal component. This is followed by automatically measuring an evoked compound action potential (ECAP) of the recipient evoked in response to the application of the neural response component of the first stimulus signal. The method further includes simultaneously providing to a user an indication of the automatically measured ECAP and a value of the applied psychophysics signal component.

In an exemplary embodiment of the present invention, the action of applying to at least one stimulation channel of the cochlear implant a first stimulus signal comprising a neural response component and a psychophysics signal component comprises applying the neural response component in a burst and applying the psychophysics signal component in a burst such that the psychophysics burst sounds, to the recipient, much louder than the applied neural response component. In an exemplary embodiment, the bursts are applied temporally adjacent to one another. More specifically, the psychophysics burst is applied immediately after the neural response burst. In an exemplary embodiment, the action of applying to at least one stimulation channel of the cochlear implant a first stimulus signal comprising a neural response component and a psychophysics signal component comprises applying the neural response component in a burst, applying the psychophysics signal component in a burst such that the psychophysics burst sounds, to the recipient, about the same loudness as the applied neural response component.

Further, an exemplary embodiment of the method described above includes automatically analysing an evoked neural response to determine the threshold Neural Response Telemetry (T-NRT) while avoiding the above and other drawbacks of conventional approaches. Generally, the systems, methods, techniques and approaches of the present invention apply electrical stimulation to a target neural region at incrementally greater current levels beginning with an initial current level that is as close as possible to a typical T-NRT level; record an NRT measurement of an auditory signal which generated by the target neural region in response to the stimulation; and utilize a machine-learned expert system to predict whether the NRT measurement contains a neural response based on a plurality of features extracted from the auditory signal.

In one embodiment, the initial current level is selected to insure safety while minimizing the quantity of measurements required to determine the T-NRT. As such, in post-operative applications, the initial current level is substantially below the typical T-NRT and at a current level at which a neural response is not expected to be evoked while in intraoperative applications, the initial current level is below but close to the typical T-NRT. Where it is evaluated by the decision tree that a neural response has not been evoked, the amplitude or current level of the neural stimulus is preferably incremented and the method repeated. Such embodiments provide for the amplitude or current level of the applied stimuli to be gradually increased until the expert system evaluates that a neural response has been evoked. The threshold is then locally established, preferably at a finer stimulus resolution.

An embodiment of the present invention does not require making recordings at supra-threshold stimulation levels; that is, the T-NRT values are obtained at a stimulation current level that rarely exceeds the recipient's maximum comfortable level of stimulation. Rather, embodiments of the present invention approach threshold from about the same stimulation levels and stop as soon as a confident neural response is established.

An embodiment of the present invention utilizes an expert system that considers a variety of extracted features. This is in contrast to the known conventional systems in which measurement of the neural response requires an expert operator to provide an assessment of the obtained neural response measurement based on both audiological skills and cumulative experience in interpretation of specific neural response measurements. In contrast, the present invention analyses the measured neural responses automatically and accurately without the contribution of an expert user.

Embodiments of the present invention are generally directed to the automatic measurement of evoked compound action potential (ECAP) thresholds of the auditory nerve; that is, a neural response, concurrently with the performance of psychophysics measurements of a prosthetic hearing implant recipient. As noted, psychophysics measurements are performed to establish recipient-specific parameters (such as threshold and comfort levels) for each of a plurality of stimulation channels.

During the fitting process, a stimulus signal comprising two components is applied to each stimulation channel. One signal component is configured to elicit an ECAP neural response, referred to herein as a neural response signal component. The other component is configured to elicit a response to a psychophysics stimulation, referred to herein as a psychophysics signal component. There is no particular relationship between the neural response and psychophysics signal components. For example, the neural response signal component may precede or follow the psychophysics signal component, the time duration of the neural response signal component may be greater than or less than that of the psychophysics signal component, and so on.

In certain embodiments, indications of the psychophysics measurement and the concurrently obtained neural response measurements are provided to the user. For the psychophysics measurement, this includes the selected characteristics of the psychophysics signal component since the results of the applied stimulation is a recipient behavioral or auditory response.

This enables the neural response measurements to be used as an objective basis to judge the subjective determinations of the concurrently-determined psychophysics measurements. This may increase the clinician's confidence in executing the fitting process, facilitating the efficient creation of the recipient MAP. Such features may be attained with minimal drawbacks. The neural response stimulus is neither perceivable by the recipient nor does it interfere with the psychophysics measurements. Furthermore, the neural response measurements are performed automatically and without clinician involvement. Thus, the concurrence performance of neural response measurements occurs in the background during the performance of psychophysics measurements.

Exemplary embodiments of the present invention are further described below in conjunction with the implanted component of a cochlear implant, such as a Contour™ Freedom™, Nucleus™ or Cochlear implant sold by Cochlear Limited, Australia. Such devices are described in U.S. Pat. Nos. 4,532,930, 6,537,200, 6,565,503, 6,575,894, and 6,697,674. It should be understood to those of ordinary skill in the art that embodiments of the present invention may be used in other prosthetic hearing implants and, more generally, in other stimulating medical devices such as neurostimulators, cardiac pacemakers, defibrillators, etc.

Before describing the features of the present invention, it is appropriate to briefly describe the construction of a cochlear implant system with reference to FIG. 3A.

FIG. 3A is a pictorial representation of a cochlear implant system in accordance with one embodiment of the present invention. Cochlear implant system 300 comprises a cochlear implant coupled to an automatic neural response measurement system in which embodiments of the present invention may be implemented.

Referring to FIG. 3A, the relevant components of outer ear 301, middle ear 305 and inner ear 307 are described next below. In a fully functional ear outer ear 301 comprises an auricle 310 and an ear canal 302. An acoustic pressure or sound wave 303 is collected by auricle 310 and channelled into and through ear canal 302. Disposed across the distal end of ear cannel 302 is a tympanic membrane 304 which vibrates in response to acoustic wave 303. This vibration is coupled to oval window or fenestra ovalis 312 through three bones of middle ear 305, collectively referred to as the ossicles 306 and comprising the malleus 308, the incus 309 and the stapes 311. Bones 308, 309 and 311 of middle ear 305 serve to filter and amplify acoustic wave 303, causing oval window 312 to articulate, or vibrate. Such vibration sets up waves of fluid motion within cochlea 316. Such fluid motion, in turn, activates tiny hair cells (not shown) that line the inside of cochlea 316. Activation of the hair cells causes appropriate nerve impulses to be transferred through the spiral ganglion cells and auditory nerve 314 to the brain (not shown), where they are perceived as sound. In deaf persons, there is an absence or destruction of the hair cells. Prosthetic hearing implant 300 is utilized to directly stimulate the ganglion cells to provide a hearing sensation to the recipient.

Conventional cochlear implant system 300 comprises external component assembly 343 which is directly or indirectly attached to the body of the recipient, and an internal component assembly 344 which is temporarily or permanently implanted in the recipient. External assembly 343 typically comprises microphone 324 for detecting sound, a speech processing unit 326, a power source (not shown), collectively assembled together in a Behind-The-Ear (BTE) Unit, and an external transmitter unit 328. External transmitter unit 328 comprises an external coil 330 and, preferably, a magnet (not shown) secured directly or indirectly to the external coil. Speech processing unit 326 processes the output of audio pickup devices 324 that are positioned, in the depicted embodiment, by ear 310 of the recipient. Speech processing unit 326 generates coded signals, referred to herein as a stimulation data signals, which are provided to external transmitter unit 328 via a cable (not shown). Speech processing unit 326 is, in this illustration, constructed and arranged so that it can fit behind the outer ear 310. Alternative versions may be worn on the body or it may be possible to provide a fully implantable system which incorporates the speech processor and/or microphone into the implanted stimulator unit.

Internal components 344 comprise an internal receiver unit 332, a stimulator unit 320, and an electrode assembly 318. Internal receiver unit 332 comprises an internal transcutaneous transfer coil (not shown), and preferably, a magnet (also not shown) fixed relative to the internal coil. Internal receiver unit 332 and stimulator unit 320 are hermetically sealed within a biocompatible housing. The internal coil receives power and data from external coil 330, as noted above. A cable or lead of electrode assembly 318 extends from stimulator unit 320 to cochlea 316 and terminates in an array of electrodes 342. Signals generated by stimulator unit 320 are applied by electrodes of the array of electrodes 342 to cochlear 316, thereby stimulating the auditory nerve 314. The array of electrodes 342 enters cochlea 316 at cochleostomy region 322 and has one or more electrodes positioned to substantially be aligned with portions of tonotopically-mapped cochlea 316. Signals generated by stimulator unit 320 are applied by the electrodes to cochlea 316, thereby stimulating auditory nerve 314.

In one embodiment, external coil 330 transmits electrical signals to the internal coil via a radio frequency (RF) link. The internal coil is typically a wire antenna coil comprised of at least one and preferably multiple turns of electrically insulated single-strand or multi-strand platinum or gold wire. The electrical insulation of the internal coil is provided by a flexible silicone molding (not shown). In use, implantable receiver unit 332 may be positioned in a recess of the temporal bone adjacent ear 310 of the recipient.

It should be appreciated that although in the embodiment shown in FIG. 3A, the electrodes are arranged in an array 346, other arrangements are possible. Further details of a convention cochlear implant device may be found in U.S. Pat. Nos. 4,532,930, 6,537,200, 6,565,503, 6,575,894 and 6,697,674, which under some circumstances may be utilized to practice certain embodiments of the present invention.

Speech processing unit 326 of cochlear implant system 300 performs an audio spectral analysis of acoustic signals 303 and outputs channel amplitude levels. Speech processing unit 326 can also sort the outputs in order of magnitude, or flag the spectral maxima as used in the SPEAK strategy developed by Cochlear Ltd.

The CI24M and CI24R model cochlear implants commercially available from Cochlear Ltd. are built around the CIC3 Cochlear Implant Chip. The CI24RE cochlear implant, also commercially available from Cochlear Ltd., is built around the CIC4 Cochlear Implant Chip. Cochlear implants based on either the CIC3 or CIC4 Cochlear Implant Chip allow the recording of neural activity within cochlea 316 in response to electrical stimulation by the array of electrodes 342. Such Neural Response Telemetry (NRT) provides measurements of the Electrically-evoked Compound Action Potentials (ECAPs) from within cochlea 316. Generally, the neural response resulting from a stimulus presented at one electrode of the array of electrodes 342 is measured at a neighbouring electrode, although this need not be the case.

As detailed above, the effectiveness of a prosthetic hearing implant can be improved based on the way in which the device is configured or “fit” for the recipient. Fitting of a device, also referred to as “programming” or “mapping,” creates a set of instructions that defines the specific characteristics used to stimulate electrodes of the implanted electrode array 342. This set of instructions is referred to as the recipient's “program” or “MAP.” FIG. 3B is a schematic diagram illustrating one exemplary fitting arrangement 360 in which a fitting system 366 is utilized to fit cochlear implant 300 to a recipient 362. Fitting System 366 performs one or more of the functions of mapping, neural response measuring, acoustic stimulating, and recording of neural response measurements and other stimuli. As one of ordinary skill in the art would appreciate, the characteristics and code transmitted by cochlear implant 300 are dependent in part on the effectiveness with which the implant is fit to an individual recipient 362.

As shown in FIG. 3B, an audiologist or clinician 364 uses a hearing implant fitting system 366 (“fitting system” herein) comprising interactive software and computer hardware to create individualized recipient map data 382 that are digitally stored on system 366 and ultimately downloaded to the memory of speech processor 326 of recipient 362. The Audiologist or clinician 364 can input control input 170 into the system 366. System 366 is programmed and/or implements software programmed to carry out one or more of the functions of mapping, neural response measuring, acoustic stimulating, and recording of neural response measurements and other stimuli.

In the embodiment illustrated in FIG. 3B, speech processor 326 of cochlear implant 300 is connected directly to fitting system 366 to establish a data communication link 368 between the speech processor and fitting system. System 366 is thereafter bi-directionally coupled by data communication link 368 with speech processor 326. It should be appreciated that although speech processor 326 and fitting system 366 are connected via a cable in FIG. 3B, any communications link now or later developed may be utilized to communicably couple the implant and fitting system.

Once cochlear implant 300 is calibrated, specific mapping data 382 is determined The particular details of the implemented fitting process are specific to the recipient, cochlear implant manufacturer, cochlear implant device, etc. As a result, only selected exemplary mapping data are described herein for clarity.

Today, most cochlear implants require at least two values to be set for each stimulating electrodes of the array of electrodes 342. These values, which were detailed above, are referred to as the threshold level (also commonly referred to as the “THR” or “T level”) and the maximum comfort level (also commonly referred to as the “Most Comfortable Loudness level,” “MCL,” “M level,” “C level,” “Maximum Comfortable Loudness,” or simply “comfort level”). Threshold levels are comparable to acoustic threshold levels; comfort levels indicate the level at which a sound is loud but comfortable. It should be appreciated that although the terminology and abbreviations are device-specific, the general purpose of threshold and comfort levels is common among all cochlear implants: to determine a recipient's electrical dynamic range.

As detailed above, in adult cochlear implant recipients, threshold and comfort levels are typically measured using verbal feedback from recipient 362. For children, who often lack the listening experience, language, or conceptual development to perform specific fitting tasks, audiologists and clinicians must often rely on clinical intuition and trial and error to appropriately estimate comfort levels for young recipients. The above and other feedback is generally referred to by reference numeral 384 in FIG. 3B. Performance data provided directly to fitting system 366 may be provided via data connection 368 as performance data 380B, while performance data provided by the audiologist/clinician based on oral feedback or observations 384 is shown in FIG. 1B as performance data 380A (performance data 380A and 380B are generally and collectively referred to herein as performance data 180).

In an exemplary embodiment, the electrical activity of the nerves in cochlea 316 which are evoked in response to the stimulation, i.e. the neural response, is detected, encoded and transmitted back to fitting system 366. In one embodiment, fitting system 366 processes the neural response data using commonly available neural response measurement software.

Embodiments of the present invention use machine learning to create the decision making algorithm that analyzes the traces of the transmitted data and makes a decision on whether or not the trace includes a neural response, and the strength of the response. In some embodiments, the concurrently-measured neural response; that is, the Evoked Compound Action Potential (ECAP), may be indicated, visually and/or audibly, concurrently and in combination with the psychophysics mapping.

The psychophysics burst will produce a stimulation that will allow the recipient to react and in turn allow the clinician to map the T and C levels using fitting system 366. Psychophysics is an approach to understanding perception that relates the characteristics of physical stimuli to attributes of the sensory experience such stimuli produce. In the exemplary application of a cochlear implant, the term “psychophysics” refers to the study of the perceptions elicited in recipients by electrical stimulation of the auditory nerve. Typically, a clinician observes a recipient that receives an electrical stimulation and judges the type of response. For example, a stimulus pulse of 100 microamps may be inaudible to the recipient, 200 microamps may be perceived by the recipient as a soft sound, and 500 microamps may be perceived by the recipient as a sound that is too loud.

An embodiment of the present invention includes a neural response telemetry device. The neural response telemetry device is a computerized system that allows for measurement of the ECAP response of the auditory nerve evoked by electrical stimulation applied by electrodes implanted in the cochlea in an embodiment of the present invention. As such, neural response telemetry allows a clinician to set a MAP that does not rely on subjective observations. In an embodiment, neural response telemetry may be used such that the recipient is not required to pay attention or remain immobile, which notably allows for use with infants. Types of neural response telemetry which may be used in certain embodiments of the present invention are described in U.S. Pat. Nos. 6,915,366 and 5,758,651. One exemplary computerized system that uses neural response telemetry usable in certain embodiments of the present invention is the Nucleus® NRT™ 3.0, commercially available from Cochlear Limited, Australia.

Traditional neural response telemetry may be subject to a number of drawbacks, drawbacks that certain embodiments of the present invention may overcome (i.e., does not suffer from some or all of these drawbacks). First, the clinician must obtain a neural response curve so as to be able to measure the amplitude of the neural response. To obtain such a response curve, a certain number of parameters related to measurement, in particular the response acquisition latency, the amplification gain, the stimulation voltage, etc., must be adjusted and optimized. These initial operations are time consuming, subjective, and the results strongly depend on the skill of the clinician. Second, when the response curves that allow for plotting of the growth function for an electrode have been obtained, the clinician must determine which of these curves can be used and which cannot. In particular, some response curves have a poor signal-to-noise ratio and are not easily identifiable. This selection is also subjective, and the results directly influence the subsequent adjustment of the implant. Third, clinicians must measure the voltages of the peaks on each response curve, to determine the response amplitude. This measurement, which is also subjective, directly influences the adjustment of the implant, and is also a lengthy and bothersome procedure. More importantly, the subjective determinations necessary for neural response telemetry may decrease its objective reliability.

Some clinicians using neural response measurements alone may experience the difficulties as discussed above, and thus may be reluctant to use systems such as Nucleus NRT. Embodiments of the present invention may eliminate or reduce this reluctance by using a neural response measurement in the background; that is, automatically, when the MAP is created. In an exemplary embodiment, neural response measurements can be performed in the background, and/or the measurements can also be performed automatically, regardless of any MAP. The background ECAP or neural response measurement does not impact the speed or accuracy using the standard psychophysics mapping procedure. Clinicians may develop judgment and become accustomed to neural response measurements by using automatic and concurrent neural response measurements along with the psychophysics. Still further, automatic and concurrent neural response measurements may provide additional objective information to expedite the fitting process. In some embodiments of the present invention, automated fitting may be achieved and be accepted by clinicians.

To provide for such measurements, an exemplary system of the present invention may comprise a prosthetic hearing implant with a neural response measurement system and recordal capabilities, software to drive the appropriate stimulus and an acoustic stimulator as shown in FIG. 3A.

An exemplary neural response measurement system and method usable in certain embodiments of the present invention will now be described. As shown in FIG. 3A, an automatic neural response measurement system 354 is communicably coupled to speech processor 326 via a cable 352. System 354 is, in one embodiment, a processor-based system such as a personal computer, server, workstation or the like, on which software programs are executed to perform the infra-threshold neural response measurements of the present invention. In addition, system 354 comprises neural response expert system(s) 350 that provide neural response threshold predictions in accordance with the teachings of the present invention.

An expert system 350 is a method of solving pattern recognition problems, based on classifications performed by a human expert of the pattern domain. By presenting a sample set of patterns and their corresponding expert classifications to an appropriate computer algorithm or statistical process, systems of various descriptions can be produced to perform the recognition task. In preferred embodiments of the present invention the expert system comprises a machine learning algorithm such as the induction of decision trees.

As one of ordinary skill in the art would appreciate, expert system(s) 350 may be implemented in an external system such as system 354 illustrated in FIG. 3A. In alternative embodiments, expert systems 350 may be implemented in speech processor 326 or in an implanted component of a partially or totally-implanted cochlear implant.

FIGS. 4A and 4B are high-level flow charts of embodiments of the present invention. Referring first to FIG. 4A, infra-threshold process 400 begins at block 402. At block 404 the initial stimulus current level is set to a level which is as close as possible to a typical threshold current level which would cause a neural response to occur. As noted, the initial current level varies with the anticipated environment (post-operative or intra-operative) and is also sufficiently high to minimize the number of NRT measurements which are to be performed. In post-operative applications the initial current level is below the anticipated T-NRT level while in intra-operative embodiments, the initial current level is above or below the anticipated T-NRT level.

At block 406 the stimulation signal is applied to the target neural region and, at block 408 the neural response is measured or recorded. During and/or subsequent to the recording of the NRT measurement, a plurality of features are extracted from the NRT measurement at block 410.

At block 412 an expert system is implemented to determine whether the NRT measurement contains a neural response. The expert system utilizes a plurality of the extracted features to make such a determination as described herein. If a neural response has not occurred (block 414) the stimulus current level is increased and the above operations are repeated. Otherwise, process 400 ceases at block 416.

In some embodiments, to avoid false positives, the amplitude or current level of the neural stimulus is preferably successively incremented until two consecutive neural stimuli have been applied both of which lead to an evaluation by the expert system that a neural response has been evoked. In such embodiments, the stimulus current level at which the first such neural response was evoked may be defined as a first minimum stimulus threshold. In such embodiments, the current level of an applied stimulus is preferably incrementally reduced from the first minimum stimulus threshold, until two consecutive stimuli have been applied both of which lead to an evaluation by the expert system that a neural response has not been evoked. The higher of such stimuli current levels at which the neural response has not been evoked is preferably defined as a second minimum stimulus threshold. Such an embodiment is illustrated in FIG. 4B, in which a second expert system is implemented to determine whether the NRT measurements taken during the descending increments contains a neural response. In one preferred embodiment, the second expert system is configured to minimize the overall error rate as compared to the first expert system which may be configured to minimize the occurrence of false positive events.

Such embodiments provide for the minimum stimulus threshold to be defined with reference to the first minimum stimulus threshold and the second minimum stimulus threshold. For example, the minimum stimulus threshold may be defined to be a current level closest to the average of the first minimum stimulus threshold and the second minimum stimulus threshold. Alternately, in embodiments where the amplitude or current level of the neural stimulus is successively incremented until two consecutive neural stimuli have been applied both of which lead to an evaluation by the decision tree that a neural response has been evoked, the minimum stimulus threshold may simply be defined to be equal to the stimulus current level at which the first such neural response was evoked.

FIG. 5A is a flowchart illustrating the primary operations performed in one embodiment of the present invention. In this exemplary embodiment, the T-NRT level of a single electrode is measured.

Process 500 commences at block 502 and at bock 504 a stimulus current level (CL) is initialized. To insure safety of the recipient, in post-operative environments the initial current level is preferably a low value at which a neural response is not expected to be evoked. Specifically, the initial current level is set to a value that is significantly below a typical threshold level (T-NRT). In one exemplary embodiment, the initial current level is set to 100 post-operatively. However, in intra-operative environments, the noted safety concerns are not applicable due to the lack of auditory response. As such, the initial current level is set to a value that is below the typical threshold level (T-NRT). In one exemplary embodiment, the initial current level is set to 160 intra-operatively. In both environments, however, the initial current level is not set to a value unnecessarily below the typical threshold level as to do so would increase the number of NRT measurements that will be performed to reach the threshold level which causes a neural response. It should also be appreciated that the initial current level may have other values in alternative embodiments. Further, in alternative embodiments, the current level is user defined.

At block 506, a clinician is asked to accept the present value of the stimulus current level. The clinician may, for example, refuse if process 500 is being applied post-operatively and the present value of the stimulus current level would exceed a recipient's comfort threshold. Refusal causes process 500 to cease at block 508. Additionally or alternatively, process 500 may be halted to avoid a violation of electrical capabilities of the components applying the neural stimulus.

Alternatively, clinician acceptance at block 506 leads to an NRT measurement being performed at block 510. The NRT measurement performed at block 510 involves application of a stimulus at the accepted stimulus current level by at least one electrode of interest. Preferably, system 354 implements a technique that removes or minimizes stimulus artefacts. For example, in one embodiment, system 354 implements a technique similar to that described in U.S. Pat. No. 5,758,651. This patent describes one conventional apparatus for recovering ECAP data from a cochlear implant. This system measures the neural response to the electrical stimulation by using the stimulus array to not only apply the stimulation but to also detect and receive the response. In this system the array used to stimulate and collect information is a standard implanted intra-cochlear and/or extra-cochlear electrode array. Following the delivery of a stimulation pulse via chosen stimulus electrodes, all electrodes of the array are open circuited for a period of time prior to and during measurement of the induced neural response. Open circuiting all electrodes during this period is to reduce the detected stimulus artefact measured with the ECAP nerve response.

In an alternative embodiment, system 354 generates a compensatory stimulus signal in a manner such as that described in WO 2002/082982 and/or WO 2004/021885. Following application of a first stimulus to a nerve, WO 2002/082982 teaches application of a compensatory stimulus closely afterwards to counteract a stimulus artefact caused by the first stimulus. In some such embodiments, automatic optimization of the compensatory stimulus is performed thereby providing automated cancellation or minimization of stimulus artefacts from measurements of the evoked neural response.

WO 2004/021885 relates to the control of a reference voltage of a neural response amplifier throughout signal acquisition to avoid the amplifier entering saturation. While variation of the reference voltage causes the output of the amplifier to be a piecewise signal, such a piecewise signal is easily reconstructed, and thus this disclosure allows an amplifier of high gain to be used to improve signal acquisition resolution. In some such embodiments, a reference voltage of an amplifier used in the measurement process is altered during the measurement in order to produce a piecewise signal which avoids saturation of the amplifier.

A neural response to such a stimulus is measured or recorded by way of an adjacent electrode and a high gain amplifier (not shown), to yield a data set of 32 voltage samples (not shown) which form the NRT measurement (also referred to as the NRT measurement waveform or trace herein).

Operations depicted in dashed block 505 are next performed to improve recording quality and, if the quality is poor, to cease recording the neural response at that electrode. At block 512, process 500 performs a voltage level compliance check to determine whether the implant can deliver the required stimulus current by providing sufficient electrode voltage. If the compliance check determines that that an error has occurred, such as by reading a flag generated by the above-noted sound processor chips, cause process 500 ceases at block 514. However, if at block 512 it is determined that the hardware is in compliance, processing continues at block 516.

At block 516 a check is made of whether clipping of the NRT amplifier occurred. In one embodiment, this too may be determined by reading a Boolean flag generated by the above-noted sound processing chips. If NRT amplifier clipping occurred, then processing continues at block 518 at which the compensatory stimulus and/or the amplifier gain is optimized. The operations performed at block 518 are described in detail below with reference to FIG. 7. Processing then returns to block 510 and the above operations are repeated.

At block 520, a machine-learned expert system is utilized to predict whether an NRT measurement contains a neural response based on the plurality of extracted auditory signal features. In one embodiment, the expert system was built using the induction of decision trees. In one implementation of such an embodiment, the induction of decision trees machine learning algorithm is the algorithm C5.0 described in Quinlan, J., 1993. “C4.5: Programs for Machine Learning.” Morgan Kaufmann, San Mateo; and Quinlan, J., 2004. “See 5: An Informal Tutorial.” Rulequest Research.

In one embodiment, the decision tree 600A illustrated in FIG. 6A is applied to the obtained 32 sample set measurement of the NRT measurement. That is, neural response expert system 350 considers or processes a plurality of features extracted from the NRT measurement to determine if it contains a “good” neural response. As used herein, a “good” neural response is one which approximates a true neural response to the applied stimulus level as determined by a sampling a statistically-significant population of recipients.

Should decision tree 600A determine that a given NRT measurement does not contain a “good” neural response and thus that a neural response has not been evoked (block 521), the process 500 continues at block 522 at which the stimulus current level CL is incrementally increased. The operations performed at block 522 are described below with reference to FIG. 5C. Process 500 then proceeds to block 506 and the above operations are repeated using this higher current level.

Should process 500 determine at block 521 that a neural response has been evoked, then at block 524 an assessment is made as to whether there is confidence in this determination. There are many ways to evaluate the confidence of the prediction made by the expert system operating at block 520. In one exemplary embodiment, process 500 determines at block 524 whether two consecutive stimuli have each evoked a neural response. If not, a variable ‘MaxT-NRT’ is set at block 525 to the applied stimulus current level for use at block 522. Process 500 then proceeds to block 522 as shown in FIG. 5A. If two consecutive stimuli have each evoked a neural response, the process 500 continues with operations depicted in FIG. 5B to accurately determine the minimum threshold stimulation current which causes a neural response. These operations are described in detail below with reference to FIG. 5B.

Referring now to FIG. 5C, the operations performed at block 522 in one embodiment of the present invention are described next below. The size of the increment in the value of the stimulus current level depends on the following decision tree predictions:

-   -   6 current level units if both the present and previous NRT         measurements are not deemed a neural response (blocks 582, 584         and 586).     -   4 current level units if the present NRT measurement is not         deemed a neural response whereas the previous measurement was         predicted otherwise (blocks 582, 586, 588).     -   2 current level units if the present NRT measurement is deemed a         neural response (blocks 582, 590).

If at block 524 it is determined that the T-NRT has been predicted with sufficient confidence, then process 500 continues with the descending series of operations illustrated in FIG. 5B. In the exemplary embodiment, an acceptable confidence level is attained if two consecutive stimuli have each evoked a neural response. When that occurs, the stimulus current level is reset to be equal to MaxT-NRT at block 552.

At block 554, a decision tree 600B, depicted in FIG. 6B, is applied to the latest 32 sample set measurement of the neural response. Should decision tree 600B determine that a neural response has been evoked, MaxT-NRT is set to the present stimulus current level value at block 555. The stimulus current level is then decremented by 6 current level units at block 580. This increment size may be another value or user definable in alternate embodiments.

In one preferred embodiment, separate decision trees 600A and 600B are used for various phases of process 500. As described above, decision tree 600A is used in the ascending phase illustrated in FIG. 5A, and is optimized for a low false positive rate. Decision tree 600B, on the other hand, is used in the descending phase of process 500 illustrated in FIG. 5B, and is optimized for a low overall error rate.

At block 572 an NRT measurement is made in the same manner as that performed at block 510, and process 500 returns to block 554. Should decision tree 600B determine that a neural response has not been evoked, the stimulus current level is reset to MaxT-NRT at block 556.

At block 558, the current level is decremented by 2 current level units, which is a smaller interval than the increment applied at block 522, and the decrement applied at block 530.

At block 560, an NRT measurement is again performed during the CL decrementing stage, in the same manner as the NRT measurement performed at block 510.

At block 562 the decision tree 600B is again applied to the obtained 32-sample NRT measurement, in order to determine whether a neural response has been evoked by application of the stimulus at the present current level. If so, the algorithm returns to step 558. At block 563 MaxT-NRT is set to the present value of the stimulus current level if decision tree 600B has always deemed that a neural response has been evoked since block 556.

If decision tree 600B determines that a neural response has not been evoked, then at block 564 a determination is made as to whether two consecutive stimuli have not evoked a neural response. If there have not been two consecutive stimuli which have not evoked a neural response, a variable ‘MinT-NRT’ is set to be equal to the present value of CL, and process 500 returns to block 558. If there has been two consecutive stimuli which have not evoked a neural response, then at block 566 a T-NRT value is determined from the two variables of MaxT-NRT and MinT-NRT, in the manner described below with reference to FIG. 8. Process 500 then ceases at block 568.

If the current level of any stimulation pulse [probe, masker, etc.] ever exceeds its range, the measurement is stopped.

In one embodiment, the algorithm is further optimised such that no NRT measurement is repeated at a given current level throughout the algorithm. Previous measurements may be used if they exist for the required current level.

FIG. 6A illustrates one embodiment of a decision tree used in the embodiment of FIG. 5A to determine whether a neural response has been evoked. FIG. 6B illustrates one embodiment of a decision tree used in the embodiment of FIG. 5B to determine whether a neural response has been evoked. The utilization of two decision trees 600A and 600B to determine T-NRT is useful in some applications. In the flowchart illustrated in FIG. 5A, the stimulation current level is incrementally increased and T-NRT has not yet been predicted. In such a process, decision tree 600A is utilized to provide a low false-positive rate so that a neural response can be predicted with a high degree of confidence. Thereafter, while descending at finer increments in the flowchart illustrated in FIG. 5B, decision tree 600B is utilized due to its ability to more accurately predict a neural response has occurred.

Each parameter considered in decision tree structure or dichotomous key 600A is defined herein below. As one of ordinary skill in the art would appreciate, the use of the terms attributes, parameters, features and the like are commonly used interchangeably to refer to the raw and calculated values utilized in a decision tree. The selection of such terms herein, then, is solely to facilitate understanding. It should also be appreciated that the first occurring peak positive and negative values of an NRT measurement waveform are commonly referred to as P1 and N1, respectively, as noted above. For ease of description, these terms are utilized below. In the following description, the parameters considered at each of the decision nodes 602, 604, 606, 608, 610 and 612 are first described followed by a description of decision tree 600A.

Parameter N1P1/Noise is considered at decision node 602. Parameter N1P1/Noise represents the signal to noise ratio of the NRT measurement. As noted, in the exemplary embodiment, each NRT measurement provides a trace or waveform derived from 32 samples of the neural response obtained at a sampling rate of 20 kHz.

-   -   N1 is the minimum of the first 8 samples.     -   P1 is the maximum of the samples after N1, up to and including         sample 16.         N1−P1(μV)=ECAP_(P1)−ECAP_(N1).     -   If any of the following rules are true, N1−P1=0:         -   N1−P1<0         -   Latency between N1 and P1<2 samples         -   Latency between N1 and P1>12 samples         -   Latency between N1 and the maximum sample post-N1>15 samples             AND Ratio of N1−P1 to the range N1 onwards<0.85     -   Noise=the range (maximum minus minimum) of samples 17-32.     -   N1P1/Noise=N1−P1 (amplitude) divided by Noise (the noise level).

Parameter R_(Response) is considered at decision nodes 608 and 610. Parameter R_(Response) is defined as the correlation coefficient between the given NRT measurement and a fixed good response, calculated over samples 1-24. A predefined 32 sample standard response used in the present embodiment is shown in FIG. 9. In this embodiment, the standard correlation coefficient is utilized:

$r\; = \frac{\sum\limits_{Samples}\;{\left( {x - \overset{\_}{x}} \right)\left( {y - \overset{\_}{y}} \right)}}{\sqrt{\sum\limits_{Samples}\;{\left( {x - \overset{\_}{x}} \right)^{2}{\sum\limits_{Samples}\;\left( {y - \overset{\_}{y}} \right)^{2}}}}}$

Parameter R_(Resp+Artef) is considered a decision nodes 604 and 612. Parameter R_(Resp+Artef) is defined as the correlation coefficient between the given NRT measurement and a fixed trace with neural response plus artefact, calculated over samples 1-24. A predefined 32 sample standard response used in the present embodiment is shown in FIG. 10, with FIG. 11 illustrating a predefined expected or ‘good’ stimulus artefact.

Parameter R_(Previous) is considered a decision node 606. Parameter R_(Previous) is defined as the correlation coefficient between the given NRT measurement and the NRT measurement of immediately lower stimulus current level, calculated over samples 1-24. In one embodiment, any previously performed measurement of lower stimulus level, whether the step difference is 2CL, 6CL, etc.

As shown in FIG. 6A, when N1P1/Noise is zero, decision tree 600A predicts that the NRT measurement does not contain a neural response as illustrated by decision node 601. Should N1P1/Noise 602 have a value between 0.0 and 1.64, then the value of parameter R_(Resp+Artef) is considered at decision node 604. Similarly, should N1P1/Noise have a value greater than 1.64, then the value of parameter R_(Previous) is considered at decision node 606.

At decision node 604 of parameter R_(Resp+Artef) is considered. If it determined to be less than or equal to 0.87, then parameter R_(Response) is determined at decision node 608. However, if R_(Resp+Artef) is determined to be greater than 0.87, then a different consideration of parameter R_(Response) is performed at decision node 610.

Returning to decision node 606 at which parameter R_(Previous) is considered. If the parameter is less than or equal to 0.38, then decision tree 600A determines that the given NRT measurement fails to contain a neural response, as indicated at block 603 of FIG. 6A. However, if the parameter is greater than 0.38, then decision tree 600A determines that the given NRT measurement does contain a neural response, as indicated at block 605 of FIG. 6A. Thus, if the parameter N1P1/Noise is greater than 1.64 and the parameter R_(Previous) is greater than 0.38, then the NRT measurement is predicted to contain a neural response.

At decision node 608 decision tree 600A considered whether parameter R_(Response) is less than or equal to 0.43, in which case decision tree 600A predicts that the NRT measurement does not contain a neural response, as shown at block 607. At decision node 608 decision tree 600A also considers whether parameter R_(Response) is greater than 0.62, at which decision tree 600A predicts that the NRT measurement does contain a neural response, as shown at block 609. Thus, if the parameter N1P1/Noise is greater than zero and less than or equal to 1.64, parameter R_(Resp+Artef) is less than or equal to 0.87 and parameter R_(Response) is less than 0.62, then decision tree 600A predicts that the NRT measurement contains a neural response.

At decision node 610 decision tree 600A considered whether parameter R_(Response) is less than or equal to 0.01, in which case decision tree 600A predicts that the NRT measurement does not contain a neural response, as shown at block 611. At decision node 610 decision tree 600A also considers whether parameter R_(Response) is greater than 0.01, at which decision tree 600A predicts that the NRT measurement does contain a neural response, as shown at block 613. Thus, if the parameter N1P1/Noise is greater than zero and less than or equal to 1.64, parameter R_(Resp+Artef) is greater than 0.87, and parameter R_(Response) is greater than 0.01, then decision tree 600A predicts that the NRT measurement contains a neural response.

Returning to decision node 608, decision tree 600A also considers whether parameter R_(Response) is greater than 0.43 and less than or equal to 0.62. If so, decision tree 600A considers parameter R_(Resp+Artef) at decision node 612. There, if R_(Resp+Artef) is less than or equal to 0.56, then decision tree 600A predicts that the NRT measurement does not contain a neural response, as indicated at block 615. Alternatively, if R_(Resp+Artef) is greater than 0.56, then decision tree 600A predicts that the NRT measurement contain a neural response, as indicated at block 617. Thus, if the parameter N1P1/Noise is greater than zero and less than or equal to 1.64, parameter R_(Resp+Artef) is less than or equal to 0.87, parameter R_(Response) is greater than 0.43 and less than or equal to 0.62, and parameter R_(Resp+Artef) is greater than 0.56, then decision tree 600A predicts that the NRT measurement contains a neural response.

As one or ordinary skill in the art would appreciate, the above values are exemplary only. For example, in one alternative embodiment, N1 is determined based on a quantity of sampled other than eight. Similarly, the positive peak occurs after the negative peak in NRT measurement waveforms. In the above embodiment, the positive peak is limited to the maximum sample after the first occurring negative peak N1. However, because the trailing portion of an NRT waveform is generally level and should not contain a pulse. It should be appreciated, however, that in alternative embodiments, P1 is defined as the maximum sample which occurs after N1 and less than 14-18 samples. Similarly, the latency between the first occurring negative and positive peaks may be other than 2 and 12 samples in alternative embodiments. And so on.

Referring now to FIG. 6B, decision tree 600B will be described. The parameters or features considered or evaluated at decision blocks 652, 54, 656, 658, 660, 662 and 664 are described above.

At decision node 652 parameter N1P1/Noise is considered by decision tree 600B. If the parameter N1P1/Noise zero, decision tree 600B predicts that the NRT measurement does not contain a neural response as illustrated by decision node 651. Should the parameter N1P1/Noise have a value greater than 0.0 and less than or equal to 1.41, then the value of parameter R_(Resp+Artef) is considered at decision node 654. Similarly, should the parameter N1P1/Noise have a value greater than 1.41, then the value of parameter R_(Response) is considered at decision node 656.

At decision node 654, parameter R_(Resp+Artef) is considered. If this parameter determined to be less than or equal to 0.87, then parameter R_(Response) is considered at decision node 660. However, if R_(Resp+Artef) is determined to be greater than 0.87, then a different consideration of parameter R_(Response) is performed at decision node 662.

Returning to decision node 656 at which parameter R_(Response) is considered. If the parameter is less than or equal to 0.57, then decision tree 600B considers the parameter R_(Previous) at decision node 658. However, if the parameter R_(Previous) is greater than 0.57, then decision tree 600B determines that the given NRT measurement contains a neural response, as indicated at block 657 of FIG. 6B. Thus, if the parameter N1P1/Noise is greater than 1.41 and the parameter R_(response) is greater than 0.57, then the NRT measurement is predicted to contain a neural response.

Returning to decision node 658 at which parameter R_(Previous) is considered. If this parameter is less than or equal to 0.57, then decision tree 600B determines that the given NRT measurement fails to contain a neural response, as indicated at block 663 of FIG. 6B. However, if this parameter is greater than 0.57, then decision tree 600B determines that the given NRT measurement does contain a neural response, as indicated at block 655 of FIG. 6B. Thus, if the parameter N1P1/Noise is greater than 1.41, the parameter R_(Response) is less than or equal to 0.57, and the parameter R_(previous) is greater than 0.57, then the NRT measurement is predicted to contain a neural response.

At decision node 660 decision tree 600B considered whether parameter R_(Response) is less than or equal to 0.28, in which case decision tree 600B predicts that the NRT measurement does not contain a neural response, as shown at block 659. At decision node 608 decision tree 600B also considers whether parameter R_(Response) is greater than 0.62, in which case decision tree 600B predicts that the NRT measurement does contain a neural response, as shown at block 661. Thus, if the parameter N1P1/Noise is greater than zero and less than or equal to 1.41, parameter R_(Resp+Artef) is less than or equal to 0.87, and parameter R_(Response) is greater than 0.62, then decision tree 600B predicts that the NRT measurement contains a neural response.

At decision node 662 decision tree 600B considered whether parameter R_(Response) is less than or equal to 0.013, in which case decision tree 600B predicts that the NRT measurement does not contain a neural response, as shown at block 667. At decision node 662 decision tree 600B also considers whether parameter R_(Response) is greater than 0.013, in which case decision tree 600B predicts that the NRT measurement does contain a neural response, as shown at block 669. Thus, if the parameter N1P1/Noise is greater than zero and less than or equal to 1.41, parameter R_(Resp+Artef) is greater than 0.87, and parameter R_(Response) is greater than 0.013, then decision tree 600B predicts that the NRT measurement contains a neural response.

Returning to decision node 660, decision tree 600B also considers whether parameter R_(Response) is greater than 0.43 and less than or equal to 0.62. If so, decision tree 600B considers parameter R_(Resp+Artef) at decision node 664. There, if the parameter R_(Resp+Artef) is less than or equal to 0.60, then decision tree 600B predicts that the NRT measurement does not contain a neural response, as indicated at block 663. Alternatively, if R_(Resp+Artef) is greater than 0.60, then decision tree 600B predicts that the NRT measurement contains a neural response, as indicated at block 665. Thus, if the parameter N1P1/Noise is greater than zero and less than or equal to 1.41, parameter R_(Resp+Artef) is less than or equal to 0.87, parameter R_(Response) is greater than 0.28 and less than or equal to 0.62, and parameter R_(Resp+Artef) is greater than 0.60, then decision tree 600B predicts that the NRT measurement contains a neural response.

As one or ordinary skill in the art would appreciate, the above values are exemplary only, and that other decision trees with other parameters and decision values may be implemented.

FIG. 7 is a flow chart of one embodiment of the primary operations performed to optimize the artefact reduction pulse (3e phase) and/or amplifier gain to avoid amplifier saturation in block 518 of FIG. 5A. After process 518 begins at block 702, it is initially attempted to optimise 3e phase with relaxed criteria at block 704.

If 3e phase optimisation does not converge (block 706) or if amplifier clipping still occurs (block 714), and process 518 continues at decision block 710 at which the gain is measured. If the gain is greater than 40 dB, then at block 716 the gain is decreased by 10 dB and number of sweeps is increased by a factor of 1.5. On the other hand, if the gain is not greater than 40 dB (block 710), automated T-NRT is cancelled for the electrode.

The NRT is measured again at block 718 and amplifier clipping is evaluated at block 720. If amplifier clipping 720 still occurs, process 518 returns to block 704 and the above optimization process is repeated.

Returning to block 706, if the 3e phase optimization converged (block 706) and if amplifier clipping ceases (block 714), then process 518 ceases at block 722. Similarly, if at block 722 amplifier clipping ceases after the optimizations made at block 716, then operation 518 also ceases at block 722.

FIG. 8 is a flowchart illustrating the derivation of the minimum stimulus threshold T-NRT from MaxT-NRT and MinT-NRT values, in accordance with one embodiment of the present invention, introduced above with reference to block 566 of FIG. 5B. In this embodiment, the minimum T-NRT level is interpolated based on the intermediate results of the automated T-NRT measurement.

After start block 802, process 566 advances to block 804, at which the difference between the Maximum T-NRT and the minimum T-NRT is measured. If it is less than or equal to 10, then the result is deemed to be confident within ±5 current levels, and a final value is output at block 806. Otherwise, if a confident result cannot be determined by the automated T-NRT algorithm, process 566 continues at block 808 at which a “?” flag is returned.

An exemplary embodiment of a two-component stimulus signal generated in accordance with the teachings of the present invention is shown in FIG. 12. The two-component stimulus signal 1200 comprises a neural response stimulus signal component 1202 and a psychophysics stimulus signal component 1204.

As shown in FIG. 12, psychophysics stimulus signal component 1204 comprises a series of pulses, collectively referred to as a psychophysics burst each having a period of 1/map stimulation rate. Neural response stimulus signal component 1202 comprises a series of pulses each having a period of 1/neural response stimulation rate.

The neural response stimulation rate may be greater or less than the psychophysics stimulation rate. In one embodiment, on a single stimulation channel neural response stimulation rate is measured approximately 80 Hz, and the psychophysics response is measured at a higher rate, referred to as the map rate. In one embodiment, the map rate is approximately 5000 Hz. In another embodiment, the map rate is approximately 2500 Hz.

Psychophysics stimulus signal component 1204 may be at the current level CL1 and each pulse of the neural response stimulation signal component 1202 may have an amplitude of greater than CL1. It should be appreciated, however, that the relative and absolute amplitude of the pulses of neural response stimulation signal component 1202 and psychophysics stimulation signal component 1204 may be different in other embodiments of the present invention.

Neural response stimulation signal component 1202 has three buffers, 1206A, 1206B and 1206C. Each buffer 1206A/1206B/1206C may be repeated n times. For example using forward masking paradigm, 5 neural response measurements may be suitable for each buffer and would be repeated 5 times for a total of 15 frames before psychophysics burst 1204.

Because the psychophysics burst rate is much higher than the rate of neural response signal 1202 (on the average of 900 to 122 Hz), neural response measurement signal 1202 will hardly influence the loudness percept, since psychophysics burst 1204 will sound much louder. The different in loudness percept may ensure that both T and C level estimates will be based on the psychophysics burst and not on the neural response recording.

Additional variations the parameters of two-component stimulus signal 1200 are readily envisaged by the embodiments of the present invention. The clinician may set the parameters based on experience or using the default parameter set by the software. In particular, psychophysics burst 1204 may be alternated depending on the current level, channel of stimulation and recipient.

FIGS. 13A, 13B and 13C are displays generated by an embodiment of the present invention. Each display 1300A, 1300B and 1300C providing concurrent presentation, in a visual form, of an indication of an evoked neural response or ECAP measurement and the applied psychophysics mapping scale. Each display 1300A/1300B/1300C includes a neural response display 1302 in which the measured ECAP is displayed, and a psychophysics mapping scale display 1304 in which the applied psychophysics mapping scale is displayed. In this illustrative embodiment, the strength of the neural response is displayed using a linear strength indicator 1306, as depicted in FIGS. 13B and 13C. In addition, an audible signal (not shown) may be beneficial when a clinician is observing a recipient's behavior and not watching display 1300A/1300B/1300C. Display 1300A/1300B/1300C enables a clinician to concurrently evaluate the T and C thresholds on a channel set by psychophysics in combination with an indication of the neural response of the same auditory nerve.

Generally, the systems, methods, techniques and approaches of the present invention apply electrical stimulation to a target neural region at incrementally greater current levels beginning with an initial current level that is below to a typical threshold neural response level; record a neural response measurement of an auditory signal which is generated by the target neural region in response to the stimulation; and determine whether the neural response measurement contains a neural response based on a plurality of features extracted from the auditory signal. In one embodiment, the expert system may be built using the induction of decision trees as disclosed in PCT Application No. PCT/US05/21204, entitled “Automatic Determination of the Threshold of an Evoked Neural Response.” In one implementation of such an embodiment, the induction of decision trees machine learning algorithm is the algorithm C5.0 described in Quinlan, J., 1993. “C4.5: Programs for Machine Learning.” Morgan Kaufmann, San Mateo; and Quinlan, J., 2004. “See 5: An Informal Tutorial.” Rulequest Research.

FIGS. 13A, 13B and 13C show a sequence of several screenshots 1300A/1300B/1300C of one embodiment of the present invention. Each display 1300A/1300B/1300C shows one or more psychophysics mapping scales 1304 for a stimulation channel 22 of electrode array 142. The current level (CL) is shown by a marker bar 208 on mapping scale 1304. The current level may be adjusted by the clinician using a clicker, keyboard, mouse, touch screen or other user interface to move marker 208.

In FIG. 13A, a low current level stimulation is set by marker 1308A on mapping scale 1304 and no neural response is found as indicated by neural response strength indicator 1306 in neural response display 1302. This is depicted by the absence of any neural strength in linear strength indicator 1306 (as opposed to FIGS. 13B and 13C).

In FIG. 13B, a relatively higher current level is used by setting marker 1308B on mapping scale 1304B above the level the marker was set on mapping scale 1304. This produces a neural response as indicated by neural response strength indicator 1306 in neural response display 1302. This is depicted by the few bars representing the measured neural strength in linear strength indicator 1306.

In FIG. 13C, a still relatively higher current level is used by setting marker 1308C on mapping scale 1304C above the levels for mapping scales 1304A and 1304B. Still further, the current level may be higher by setting marker 1308D on mapping scale 1304D above the levels for mapping scales 1304A, 1304B and 1304C. This is depicted by the increase in the number of bars representing the measured neural strength in linear strength indicator 1306.

Clinician may for instance, depending on recipient's response to the psychological burst, choose the current level shown by mapping scale 1304B in FIG. 13B as the T level, and current level shown by mapping scale 1304C as the C level for the initial MAP. In addition, assuming that the recipient responds to the current level shown in mapping scale 1304A, the clinician has objective evidence that the T level may be too low since no neural response is indicated. This may prevent an improper fitting of the prosthetic hearing implant.

In the embodiments illustrated in FIGS. 13A-13C, a visual neural response indicator is implemented as a linear strength indicator 1306. As one of ordinary skill in the art would appreciate, a myriad of other display elements may be utilized to visually convey the strength of the neural response. In one embodiment, for example, a series of individual display buttons or icons, with the quantity that have changed in display (color, intensity, etc.) reflecting the strength of the neural response. In other embodiments, dials, numerical values, graphs (bar graphs, line graphs, etc.), pie charts, etc., may be implemented, with location, quantity, color, intensity or other display characteristic selected to assist in the conveyance of neural response strength.

It should also be appreciated that audio indications of neural response strength may be implemented in addition to or as an alternative to the visual display noted above. Such an audio indication may include the alteration of audio characteristics to reflect neural response strength including, but not limited to, volume, quantity, tones, frequency, etc.

In one embodiment, the present embodiment is implemented in automated T-NRT measurements using clinical and electrophysiological software. In alternative embodiments, the present invention is implemented in software, hardware or combination thereof.

It will be appreciated by persons skilled in the art that numerous variations and/or modifications may be made to the invention as shown in the specific embodiments without departing from the spirit or scope of the invention as broadly described. The present embodiments are, therefore, to be considered in all respects as illustrative and not restrictive.

For example, in an alternative embodiment, a gain of the amplifier may be altered between application of successive stimuli. Such embodiments provide for automated optimization of the gain of the amplifier to maximize signal resolution while avoiding amplifier saturation.

Any discussion of documents, acts, materials, devices, articles or the like which has been included in the present specification is solely for the purpose of providing a context for the present invention. It is not to be taken as an admission that any or all of these matters form part of the prior art base or were common general knowledge in the field relevant to the present invention as it existed before the priority date of each claim of this application.

Throughout this specification the word “comprise”, or variations such as “comprises” or “comprising”, will be understood to imply the inclusion of a stated element, integer or step, or group of elements, integers or steps, but not the exclusion of any other element, integer or step, or group of elements, integers or steps.

The invention described and claimed herein is not to be limited in scope by the specific preferred embodiments herein disclosed, since these embodiments are intended as illustrations, and not limitations, of several aspects of the invention. Any equivalent embodiments are intended to be within the scope of this invention. Indeed, various modifications of the invention in addition to those shown and described herein will become apparent to those skilled in the art from the foregoing description. Such modifications are also intended to fall within the scope of the appended claims. 

1. A method of fitting to a recipient a cochlear implant including a plurality of stimulation channels, comprising: applying to at least one stimulation channel of the cochlear implant a first stimulus signal comprising a neural response component and a psychophysics signal component; automatically measuring an evoked compound action potential (ECAP) of the recipient evoked in response to the application of the neural response component of the first stimulus signal; and simultaneously providing to a user an indication of the automatically measured ECAP and a value of the applied psychophysics signal component.
 2. The method of claim 1, further comprising: comparing (i) an evoked response by the recipient to at least the psychophysics signal component to (ii) the automatically measured ECAP.
 3. The method of claim 2, further comprising: reapplying to the at least one stimulation channel of the cochlear implant a second stimulus signal comprising a neural response component and a psychophysics signal component, wherein a current of the psychophysics signal component of the second stimulus signal is higher than a current of the psychophysics signal of the first stimulus signal.
 4. The method of claim 3, further comprising: comparing (i) an evoked response by the recipient to at least the psychophysics signal component of the second stimulus signal to (ii) the automatically measured ECAP of the second stimulus signal.
 5. The method of claim 4, further comprising: establishing an initial MAP for the cochlear implant based on the comparison of the (i) evoked response by the recipient to at least the psychophysics signal component of the second stimulus signals to (ii) the automatically measured ECAP of the second stimulus signal, wherein establishing the initial MAP for the cochlear implant includes setting at least one of a T level of the MAP or a C level of the MAP to correspond to the current of the psychophysics signal component of the second stimulus signal.
 6. The method of claim 1, further comprising: determining that the measured ECAP of the recipient evoked in response to the application of the neural response component of the first stimulus signal is indicative of no neural response by the recipient to the neural response component of the first stimulus signal; upon the determination that the measured ECAP of the recipient evoked in response to the application of the neural response component of the first stimulus signal is indicative of no neural response by the recipient to the neural response component of the first stimulus signal, establishing an initial MAP for the cochlear implant by setting a T level of the MAP to correspond to a current that is higher than a current of the psychophysics signal component of the first stimulus signal; and evoking a response by the recipient to the psychophysics signal component of the second stimulus signal.
 7. The method of claim 1, wherein the action of applying to at least one stimulation channel of the cochlear implant a first stimulus signal comprising a neural response component and a psychophysics signal component comprises: applying the neural response component in a burst; applying the psychophysics signal component in a burst such that the psychophysics burst sounds, to the recipient, about the same loudness as the applied neural response component.
 8. The method of claim 1, wherein the action of applying to at least one stimulation channel of the cochlear implant a first stimulus signal comprising a neural response component and a psychophysics signal component comprises: applying the neural response component in a burst; applying the psychophysics signal component in a burst such that the psychophysics burst sounds, to the recipient, much louder than the applied neural response component.
 9. The method of claim 1, wherein the action of applying to at least one stimulation channel of the cochlear implant a first stimulus signal comprising a neural response component and a psychophysics signal component comprises: applying the neural response component in a burst temporally adjacent to the applied psychophysics signal component.
 10. The method of claim 9, wherein the action of applying to at least one stimulation channel of the cochlear implant a first stimulus signal comprising a neural response component and a psychophysics signal component comprises: applying the neural response component in a burst and immediately thereafter applying the psychophysics signal component in a burst.
 11. The method of claim 10, the burst of the neural response component comprises: a series of pulses having a period of about one hundredth of a second.
 12. The method of claim 10, the burst of the neural response component comprises: a series of pulses having a period of about 125 milliseconds.
 13. The method of claim 10, wherein the action of applying to at least one stimulation channel of the cochlear implant a first stimulus signal comprising a neural response component and a psychophysics signal component comprises: applying the psychophysics signal component in a burst including a series of pulses having a period of about 0.2 to about 0.4 milliseconds.
 14. The method of claim 10, wherein the action of applying to at least one stimulation channel of the cochlear implant a first stimulus signal comprising a neural response component and a psychophysics signal component comprises: applying the psychophysics signal component in a burst including a series of pulses, the burst lasting about a half of a second; and applying the neural response component in a burst including a series of pulses, wherein the psychophysics burst rate is much higher than a rate of the neural response signal.
 15. An apparatus for fitting to a recipient a cochlear implant including a plurality of stimulation channels, the apparatus configured to: apply to at least one stimulation channel of the cochlear implant a first stimulus signal comprising a neural response component and a psychophysics signal component; automatically measure a strength of an evoked compound action potential (ECAP) of the recipient evoked in response to the application of the neural response component of the first stimulus signal; and simultaneously automatically provide to a user of the apparatus an indication of the automatically measured strength of the ECAP and a value of the applied psychophysics signal component.
 16. The apparatus of claim 15, wherein: the apparatus includes a display; the display of the hearing implant fitting system is configured to display a visual representation of the measured strength of the ECAP; and the display of the hearing implant fitting system is configured to display a visual representation of the value of the applied psychophysics signal component.
 17. The apparatus of claim 15, wherein the apparatus is configured to create a decision making algorithm through the use of machine learning, wherein the decision making algorithm analyzes ECAP telemetry data received from the cochlear implant to automatically measure the strength of the ECAP of the recipient evoked in response to the application of the neural response component of the first stimulus signal.
 18. The apparatus of claim 15, wherein the apparatus is configured to: apply the neural response component in a burst temporally adjacent to the applied psychophysics signal component.
 19. A method of fitting to a recipient a cochlear implant including a plurality of stimulation channels, comprising: applying a stimulus signal to a target neural region of the recipient of the cochlear implant via a stimulus signal, the stimulus signal comprising a neural response component and a psychophysics signal component, wherein applying the stimulus signal includes applying the neural response component in a burst, wherein applying the stimulus signal includes applying the psychophysics signal component in a burst, and wherein the bursts are applied temporally adjacent to each other.
 20. The method of claim 19, wherein the psychophysics signal component is applied immediately after the neural response component. 